axiom-foundation-models-ref skill

---
name: axiom-foundation-models-ref
description: Reference — Complete Foundation Models framework guide covering LanguageModelSession, @Generable, @Guide, Tool protocol, streaming, dynamic schemas, built-in use cases, and all WWDC 2025 code examples
license: MIT
compatibility: iOS 26+, macOS 26+, iPadOS 26+, axiom-visionOS 26+
metadata:
  version: "1.0.0"
  last-updated: "2025-12-03"
---

# Foundation Models Framework — Complete API Reference

## Overview

The Foundation Models framework provides access to Apple's on-device Large Language Model (3 billion parameters, 2-bit quantized) with a Swift API. This reference covers every API, all WWDC 2025 code examples, and comprehensive implementation patterns.

### Model Specifications

3B parameter model, 2-bit quantized, 4096 token context (input + output combined). Optimized for on-device summarization, extraction, classification, and generation. NOT suited for world knowledge, complex reasoning, math, or translation. Runs entirely on-device — no network, no cost, no data leaves device.

---

## When to Use This Reference

Use this reference when:
- Implementing Foundation Models features
- Understanding API capabilities
- Looking up specific code examples
- Planning architecture with Foundation Models
- Migrating from prototype to production
- Debugging implementation issues

**Related Skills**:
- `axiom-foundation-models` — Discipline skill with anti-patterns, pressure scenarios, decision trees
- `axiom-foundation-models-diag` — Diagnostic skill for troubleshooting issues

---

## LanguageModelSession

### Overview

`LanguageModelSession` is the core class for interacting with the model. It maintains conversation history (transcript), handles multi-turn interactions, and manages model state.

### Creating a Session

**Basic Creation**:
```swift
import FoundationModels

let session = LanguageModelSession()
```

**With Custom Instructions**:
```swift
let session = LanguageModelSession(instructions: """
    You are a friendly barista in a pixel art coffee shop.
    Respond to the player's question concisely.
    """
)
```

#### From WWDC 301:1:05

**With Tools**:
```swift
let session = LanguageModelSession(
    tools: [GetWeatherTool()],
    instructions: "Help user with weather forecasts."
)
```

#### From WWDC 286:15:03

**With Specific Model/Use Case**:
```swift
let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging)
)
```

#### From WWDC 286:18:39

### Instructions vs Prompts

**Instructions**:
- Come from **developer**
- Define model's role, style, constraints
- Mostly static
- First entry in transcript
- Model trained to obey instructions over prompts (security feature)

**Prompts**:
- Come from **user** (or dynamic app state)
- Specific requests for generation
- Dynamic input
- Each call to `respond(to:)` adds prompt to transcript

**Security Consideration**:
- **NEVER** interpolate untrusted user input into instructions
- User input should go in prompts only
- Prevents prompt injection attacks

### respond(to:) Method

**Basic Text Generation**:
```swift
func respond(userInput: String) async throws -> String {
    let session = LanguageModelSession(instructions: """
        You are a friendly barista in a world full of pixels.
        Respond to the player's question.
        """
    )
    let response = try await session.respond(to: userInput)
    return response.content
}
```

#### From WWDC 301:1:05

**Return Type**: `Response<String>` with `.content` property

### respond(to:generating:) Method

**Structured Output with @Generable**:
```swift
@Generable
struct SearchSuggestions {
    @Guide(description: "A list of suggested search terms", .count(4))
    var searchTerms: [String]
}

let prompt = """
    Generate a list of suggested search terms for an app about visiting famous landmarks.
    """

let response = try await session.respond(
    to: prompt,
    generating: SearchSuggestions.self
)

print(response.content) // SearchSuggestions instance
```

#### From WWDC 286:5:51

**Return Type**: `Response<SearchSuggestions>` with `.content` property

### Generation Options

See [Sampling & Generation Options](#sampling--generation-options) for `GenerationOptions` including `sampling:`, `temperature:`, and `includeSchemaInPrompt:`.

---

## Multi-Turn Interactions

### Retaining Context

```swift
let session = LanguageModelSession()

// First turn
let firstHaiku = try await session.respond(to: "Write a haiku about fishing")
print(firstHaiku.content)
// Silent waters gleam,
// Casting lines in morning mist—
// Hope in every cast.

// Second turn - model remembers context
let secondHaiku = try await session.respond(to: "Do another one about golf")
print(secondHaiku.content)
// Silent morning dew,
// Caddies guide with gentle words—
// Paths of patience tread.

print(session.transcript) // Shows full history
```

#### From WWDC 286:17:46

**How it works**:
- Each `respond()` call adds entry to transcript
- Model uses entire transcript for context
- Enables conversational interactions

### Transcript Property

```swift
let transcript = session.transcript

for entry in transcript.entries {
    print("Entry: \(entry.content)")
}
```

**Use cases**:
- Debugging generation issues
- Displaying conversation history in UI
- Exporting chat logs
- Condensing for context management

---

## isResponding Property

Gate UI on `session.isResponding` to prevent concurrent requests:

```swift
Button("Go!") {
    Task { haiku = try await session.respond(to: prompt).content }
}
.disabled(session.isResponding)
```

#### From WWDC 286:18:22

---

## @Generable Macro

### Overview

`@Generable` enables structured output from the model using Swift types. The macro generates a schema at compile-time and uses **constrained decoding** to guarantee structural correctness.

### Basic Usage

**On Structs**:
```swift
@Generable
struct Person {
    let name: String
    let age: Int
}

let response = try await session.respond(
    to: "Generate a person",
    generating: Person.self
)

let person = response.content // Type-safe Person instance
```

#### From WWDC 301:8:14

**On Enums**:
```swift
@Generable
struct NPC {
    let name: String
    let encounter: Encounter

    @Generable
    enum Encounter {
        case orderCoffee(String)
        case wantToTalkToManager(complaint: String)
    }
}
```

#### From WWDC 301:10:49

### Supported Types

**Primitives**:
- `String`
- `Int`, `Float`, `Double`, `Decimal`
- `Bool`

**Collections**:
- `[ElementType]` (arrays)

**Composed Types**:
```swift
@Generable
struct Itinerary {
    var destination: String
    var days: Int
    var budget: Float
    var rating: Double
    var requiresVisa: Bool
    var activities: [String]
    var emergencyContact: Person
    var relatedItineraries: [Itinerary] // Recursive!
}
```

#### From WWDC 286:6:18

### @Guide Constraints

`@Guide` constrains generated properties. Supports `description:` (natural language), `.range()` (numeric bounds), `.count()` / `.maximumCount()` (array length), and `Regex` (pattern matching).

```swift
@Generable
struct NPC {
    @Guide(description: "A full name")
    let name: String

    @Guide(.range(1...10))
    let level: Int

    @Guide(.count(3))
    let attributes: [String]
}
```

#### From WWDC 301:11:20

### Constrained Decoding

**How it works**:
1. `@Generable` macro generates schema at compile-time
2. Schema defines valid token sequences
3. During generation, model creates probability distribution for next token
4. Framework **masks out invalid tokens** based on schema
5. Model can only pick tokens valid according to schema
6. Guarantees structural correctness - no hallucinated keys, no invalid JSON

**From WWDC 286**: "Constrained decoding prevents structural mistakes. Model is prevented from generating invalid field names or wrong types."

**Benefits**:
- Zero parsing code needed
- No runtime parsing errors
- Type-safe Swift objects
- Compile-time safety (changes to struct caught by compiler)

### Property Declaration Order

**Properties generated in order declared**:
```swift
@Generable
struct Itinerary {
    var name: String        // Generated FIRST
    var days: [DayPlan]     // Generated SECOND
    var summary: String     // Generated LAST
}
```

**Why it matters**:
- Later properties can reference earlier ones
- Better model quality: Summaries after content
- Better streaming UX: Important properties first

#### From WWDC 286:11:00

---

## Streaming

### Overview

Foundation Models uses **snapshot streaming** (not delta streaming). Instead of raw deltas, the framework streams `PartiallyGenerated` types with optional properties that fill in progressively.

### PartiallyGenerated Type

The `@Generable` macro automatically creates a `PartiallyGenerated` nested type:

```swift
@Generable
struct Itinerary {
    var name: String
    var days: [DayPlan]
}

// Compiler generates:
extension Itinerary {
    struct PartiallyGenerated {
        var name: String?        // All properties optional!
        var days: [DayPlan]?
    }
}
```

#### From WWDC 286:9:20

### streamResponse Method

```swift
@Generable
struct Itinerary {
    var name: String
    var days: [Day]
}

let stream = session.streamResponse(
    to: "Craft a 3-day itinerary to Mt. Fuji.",
    generating: Itinerary.self
)

for try await partial in stream {
    print(partial) // Incrementally updated Itinerary.PartiallyGenerated
}
```

#### From WWDC 286:9:40

**Return Type**: `AsyncSequence<Itinerary.PartiallyGenerated>`

### SwiftUI Integration

```swift
struct ItineraryView: View {
    let session: LanguageModelSession
    let dayCount: Int
    let landmarkName: String

    @State
    private var itinerary: Itinerary.PartiallyGenerated?

    var body: some View {
        VStack {
            if let name = itinerary?.name {
                Text(name).font(.title)
            }

            if let days = itinerary?.days {
                ForEach(days, id: \.self) { day in
                    DayView(day: day)
                }
            }

            Button("Start") {
                Task {
                    do {
                        let prompt = """
                            Generate a \(dayCount) itinerary \
                            to \(landmarkName).
                            """

                        let stream = session.streamResponse(
                            to: prompt,
                            generating: Itinerary.self
                        )

                        for try await partial in stream {
                            self.itinerary = partial
                        }
                    } catch {
                        print(error)
                    }
                }
            }
        }
    }
}
```

#### From WWDC 286:10:05

### Best Practices

**1. Use SwiftUI animations**:
```swift
if let name = itinerary?.name {
    Text(name)
        .transition(.opacity)
}
```

**2. View identity for arrays**:
```swift
// ✅ GOOD - Stable identity
ForEach(days, id: \.id) { day in
    DayView(day: day)
}

// ❌ BAD - Identity changes
ForEach(days.indices, id: \.self) { index in
    DayView(day: days[index])
}
```

**3. Property order optimization**:
```swift
// ✅ GOOD - Title first for streaming
@Generable
struct Article {
    var title: String      // Shows immediately
    var summary: String    // Shows second
    var fullText: String   // Shows last
}
```

#### From WWDC 286:11:00

---

## Tool Protocol

### Overview

Tools let the model autonomously execute your custom code to fetch external data or perform actions. Tools integrate with MapKit, WeatherKit, Contacts, EventKit, or any custom API.

### Protocol Definition

```swift
protocol Tool {
    var name: String { get }
    var description: String { get }

    associatedtype Arguments: Generable

    func call(arguments: Arguments) async throws -> ToolOutput
}
```

### Example: GetWeatherTool

```swift
import FoundationModels
import WeatherKit
import CoreLocation

struct GetWeatherTool: Tool {
    let name = "getWeather"
    let description = "Retrieve the latest weather information for a city"

    @Generable
    struct Arguments {
        @Guide(description: "The city to fetch the weather for")
        var city: String
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        let places = try await CLGeocoder().geocodeAddressString(arguments.city)
        let weather = try await WeatherService.shared.weather(for: places.first!.location!)
        let temperature = weather.currentWeather.temperature.value

        let content = GeneratedContent(properties: ["temperature": temperature])
        let output = ToolOutput(content)

        // Or if your tool's output is natural language:
        // let output = ToolOutput("\(arguments.city)'s temperature is \(temperature) degrees.")

        return output
    }
}
```

#### From WWDC 286:13:42

### Attaching Tools to Session

```swift
let session = LanguageModelSession(
    tools: [GetWeatherTool()],
    instructions: "Help the user with weather forecasts."
)

let response = try await session.respond(
    to: "What is the temperature in Cupertino?"
)

print(response.content)
// It's 71˚F in Cupertino!
```

#### From WWDC 286:15:03

**How it works**:
1. Session initialized with tools
2. User prompt: "What's Tokyo's weather?"
3. Model analyzes prompt, decides weather data needed
4. Model generates tool call: `getWeather(city: "Tokyo")`
5. Framework calls `call()` method
6. Your code fetches real data from API
7. Tool output inserted into transcript
8. Model generates final response using tool output

**From WWDC 301**: "Model autonomously decides when and how often to call tools. Can call multiple tools per request, even in parallel."

### Stateful Tools

Use `class` instead of `struct` to maintain state across tool calls. The tool instance persists for the session lifetime, enabling patterns like tracking previously returned results:

```swift
class FindContactTool: Tool {
    let name = "findContact"
    let description = "Finds a contact from a specified age generation."
    var pickedContacts = Set<String>()

    @Generable
    struct Arguments {
        let generation: Generation
        @Generable
        enum Generation { case babyBoomers, genX, millennial, genZ }
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        // Fetch, filter out already-picked, return new contact
        pickedContacts.insert(pickedContact.givenName)
        return ToolOutput(pickedContact.givenName)
    }
}
```

#### From WWDC 301:18:47, 301:21:55

### ToolOutput

**Two forms**:

1. **Natural language** (String):
```swift
return ToolOutput("Temperature is 71°F")
```

2. **Structured** (GeneratedContent):
```swift
let content = GeneratedContent(properties: ["temperature": 71])
return ToolOutput(content)
```

### Tool Naming Best Practices

**DO**:
- Short, readable names: `getWeather`, `findContact`
- Use verbs: `get`, `find`, `fetch`, `create`
- One sentence descriptions
- Keep descriptions concise (they're in prompt)

**DON'T**:
- Abbreviations: `gtWthr`
- Implementation details in description
- Long descriptions (increases token count)

**From WWDC 301**: "Tool name and description put verbatim in prompt. Longer strings mean more tokens, which increases latency."

### Multiple Tools

```swift
let session = LanguageModelSession(
    tools: [
        GetWeatherTool(),
        FindRestaurantTool(),
        FindHotelTool()
    ],
    instructions: "Plan travel itineraries."
)

// Model autonomously decides which tools to call and when
```

### Tool Calling Behavior

**Key facts**:
- Tool can be called **multiple times** per request
- Multiple tools can be called **in parallel**
- Model decides **when** to call (not guaranteed to call)
- Arguments guaranteed valid via @Generable

**From WWDC 301**: "When tools called in parallel, your call method may execute concurrently. Keep this in mind when accessing data."

---

## Dynamic Schemas

### Overview

`DynamicGenerationSchema` enables creating schemas at runtime instead of compile-time. Useful for user-defined structures, level creators, or dynamic forms.

### Creating and Using Dynamic Schemas

Build properties with `DynamicGenerationSchema.Property`, compose into schemas, then validate with `GenerationSchema`:

```swift
// Build schema at runtime
let questionProp = DynamicGenerationSchema.Property(
    name: "question", schema: DynamicGenerationSchema(type: String.self)
)
let answersProp = DynamicGenerationSchema.Property(
    name: "answers", schema: DynamicGenerationSchema(
        arrayOf: DynamicGenerationSchema(referenceTo: "Answer")
    )
)

let riddleSchema = DynamicGenerationSchema(name: "Riddle", properties: [questionProp, answersProp])
let answerSchema = DynamicGenerationSchema(name: "Answer", properties: [/* text, isCorrect */])

// Validate and use
let schema = try GenerationSchema(root: riddleSchema, dependencies: [answerSchema])
let response = try await session.respond(to: "Generate a riddle", schema: schema)

let question = try response.content.value(String.self, forProperty: "question")
```

#### From WWDC 301:14:50, 301:15:10

### Dynamic vs Static @Generable

**Use @Generable when**:
- Structure known at compile-time
- Want type safety
- Want automatic parsing

**Use Dynamic Schemas when**:
- Structure only known at runtime
- User-defined schemas
- Maximum flexibility

**From WWDC 301**: "Compile-time @Generable gives type safety. Dynamic schemas give runtime flexibility. Both use same constrained decoding guarantees."

---

## Sampling & Generation Options

**Greedy (deterministic)** — use for tests and demos. Only deterministic within same model version:
```swift
let response = try await session.respond(
    to: prompt,
    options: GenerationOptions(sampling: .greedy)
)
```

**Temperature** — controls variance. `0.1-0.5` focused, `1.0` default, `1.5-2.0` creative:
```swift
let response = try await session.respond(
    to: prompt,
    options: GenerationOptions(temperature: 0.5)
)
```

#### From WWDC 301:6:14

---

## Built-in Use Cases

### Content Tagging Adapter

**Specialized adapter for**:
- Tag generation
- Entity extraction
- Topic detection

```swift
@Generable
struct Result {
    let topics: [String]
}

let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging)
)

let response = try await session.respond(
    to: articleText,
    generating: Result.self
)
```

#### From WWDC 286:19:19

### Custom Use Cases

**With custom instructions**:
```swift
@Generable
struct Top3ActionEmotionResult {
    @Guide(.maximumCount(3))
    let actions: [String]
    @Guide(.maximumCount(3))
    let emotions: [String]
}

let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging),
    instructions: "Tag the 3 most important actions and emotions in the given input text."
)

let response = try await session.respond(
    to: text,
    generating: Top3ActionEmotionResult.self
)
```

#### From WWDC 286:19:35

---

## Error Handling

### GenerationError Types

Catch `LanguageModelSession.GenerationError` cases:
- **`.exceededContextWindowSize`** — Context limit (4096 tokens) exceeded. Condense transcript or create new session.
- **`.guardrailViolation`** — Content policy triggered. Show graceful message.
- **`.unsupportedLanguageOrLocale`** — Language not supported. Check `supportedLanguages`.

#### From WWDC 301:3:37, 301:7:06

### Context Window Management

#### Strategy 1: Fresh Session
```swift
var session = LanguageModelSession()

do {
    let response = try await session.respond(to: prompt)
    print(response.content)
} catch LanguageModelSession.GenerationError.exceededContextWindowSize {
    // New session, no history
    session = LanguageModelSession()
}
```

#### From WWDC 301:3:37

#### Strategy 2: Condensed Session
```swift
do {
    let response = try await session.respond(to: prompt)
} catch LanguageModelSession.GenerationError.exceededContextWindowSize {
    // New session with some history
    session = newSession(previousSession: session)
}

private func newSession(previousSession: LanguageModelSession) -> LanguageModelSession {
    let allEntries = previousSession.transcript.entries
    var condensedEntries = [Transcript.Entry]()

    if let firstEntry = allEntries.first {
        condensedEntries.append(firstEntry) // Instructions

        if allEntries.count > 1, let lastEntry = allEntries.last {
            condensedEntries.append(lastEntry) // Recent context
        }
    }

    let condensedTranscript = Transcript(entries: condensedEntries)
    // Note: transcript includes instructions
    return LanguageModelSession(transcript: condensedTranscript)
}
```

#### From WWDC 301:3:55

### Fallback Architecture

When Foundation Models is unavailable (older device, user opted out, unsupported region), provide graceful degradation:

```swift
func summarize(_ text: String) async throws -> String {
    let model = SystemLanguageModel.default
    switch model.availability {
    case .available:
        let session = LanguageModelSession()
        let response = try await session.respond(to: "Summarize: \(text)")
        return response.content
    case .unavailable:
        // Fallback: truncate with ellipsis, or call server API
        return String(text.prefix(200)) + "..."
    }
}
```

**Architecture pattern**: Wrap Foundation Models behind a protocol so you can swap implementations:

```swift
protocol TextSummarizer {
    func summarize(_ text: String) async throws -> String
}

struct OnDeviceSummarizer: TextSummarizer { /* Foundation Models */ }
struct ServerSummarizer: TextSummarizer { /* Server API fallback */ }
struct TruncationSummarizer: TextSummarizer { /* Simple truncation */ }
```

### Nested @Generable Troubleshooting

Nested `@Generable` types must each independently conform to `@Generable`:

```swift
// ✅ Both types marked @Generable
@Generable struct Itinerary {
    var days: [DayPlan]
}

@Generable struct DayPlan {
    var activities: [String]
}

// ❌ Will fail — nested type not @Generable
@Generable struct Itinerary {
    var days: [DayPlan]  // DayPlan must also be @Generable
}
struct DayPlan { var activities: [String] }
```

**Common issue**: Arrays of non-Generable types compile but fail at runtime. Check all types in the graph.

---

## Availability

### Checking Availability

```swift
struct AvailabilityExample: View {
    private let model = SystemLanguageModel.default

    var body: some View {
        switch model.availability {
        case .available:
            Text("Model is available").foregroundStyle(.green)
        case .unavailable(let reason):
            Text("Model is unavailable").foregroundStyle(.red)
            Text("Reason: \(reason)")
        }
    }
}
```

#### From WWDC 286:19:56

### Supported Languages

```swift
let supportedLanguages = SystemLanguageModel.default.supportedLanguages
guard supportedLanguages.contains(Locale.current.language) else {
    // Show message
    return
}
```

#### From WWDC 301:7:06

### Requirements

**Device Requirements**:
- Apple Intelligence-enabled device
- iPhone 15 Pro or later
- iPad with M1+ chip
- Mac with Apple silicon

**Region Requirements**:
- Supported region (check Apple Intelligence availability)

**User Requirements**:
- User opted in to Apple Intelligence in Settings

---

## Performance & Profiling

### Foundation Models Instrument

**Access**: Instruments app → Foundation Models template

**Metrics**:
- Initial model load time
- Token counts (input/output)
- Generation time per request
- Latency breakdown
- Optimization opportunities

**From WWDC 286**: "New Instruments profiling template lets you observe areas of optimization and quantify improvements."

### Optimization: Prewarming

**Problem**: First request takes 1-2s to load model

**Solution**: Create session before user interaction

```swift
class ViewModel: ObservableObject {
    private var session: LanguageModelSession?

    init() {
        // Prewarm on init
        Task {
            self.session = LanguageModelSession(instructions: "...")
        }
    }

    func generate(prompt: String) async throws -> String {
        let response = try await session!.respond(to: prompt)
        return response.content
    }
}
```

**From WWDC 259**: "Prewarming session before user interaction reduces initial latency."

**Time saved**: 1-2 seconds off first generation

### Optimization: includeSchemaInPrompt

**Problem**: Large @Generable schemas increase token count

**Solution**: Skip schema insertion for subsequent requests

```swift
// First request - schema inserted
let first = try await session.respond(
    to: "Generate first person",
    generating: Person.self
)

// Subsequent requests - skip schema
let second = try await session.respond(
    to: "Generate another person",
    generating: Person.self,
    options: GenerationOptions(includeSchemaInPrompt: false)
)
```

**From WWDC 259**: "Setting includeSchemaInPrompt to false decreases token count and latency for subsequent requests."

**Time saved**: 10-20% per request

### Optimization: Property Order

Declare important properties first in `@Generable` structs. With streaming, perceived latency drops from 2.5s to 0.2s when title appears before full text. See [Streaming Best Practices](#best-practices) for examples.

---

## Feedback & Analytics

`LanguageModelFeedbackAttachment` lets you report model quality issues to Apple via Feedback Assistant. Create with `input`, `output`, `sentiment` (`.positive`/`.negative`), `issues` (category + explanation), and `desiredOutputExamples`. Encode as JSON and attach to a Feedback Assistant report.

#### From WWDC 286:22:13

---

## Xcode Playgrounds

### Overview

Xcode Playgrounds enable rapid iteration on prompts without rebuilding entire app.

### Basic Usage

```swift
import FoundationModels
import Playgrounds

#Playground {
    let session = LanguageModelSession()
    let response = try await session.respond(
        to: "What's a good name for a trip to Japan? Respond only with a title"
    )
}
```

#### From WWDC 286:2:28

Playgrounds can also access types defined in your app (like @Generable structs).

---

## API Quick Reference

- **`LanguageModelSession`** — Main interface: `respond(to:)` → `Response<String>`, `respond(to:generating:)` → `Response<T>`, `streamResponse(to:generating:)` → `AsyncSequence<T.PartiallyGenerated>`. Properties: `transcript`, `isResponding`.
- **`SystemLanguageModel`** — `default.availability` (`.available`/`.unavailable(reason)`), `default.supportedLanguages`, `init(useCase:)`
- **`GenerationOptions`** — `sampling` (`.greedy`/`.random`), `temperature`, `includeSchemaInPrompt`
- **`@Generable`** — Macro enabling structured output with constrained decoding
- **`@Guide`** — Property constraints: `description:`, `.range()`, `.count()`, `.maximumCount()`, `Regex`
- **`Tool` protocol** — `name`, `description`, `Arguments: Generable`, `call(arguments:) → ToolOutput`
- **`DynamicGenerationSchema`** — Runtime schema definition with `GeneratedContent` output
- **`GenerationError`** — `.exceededContextWindowSize`, `.guardrailViolation`, `.unsupportedLanguageOrLocale`

---

## Migration Strategies

### From Server LLMs

- **Migrate when**: Privacy required, offline needed, per-request costs are a concern, and use case fits (summarization/extraction/classification)
- **Stay on server when**: Need world knowledge, complex reasoning, or >4096 token context

### From Manual JSON Parsing

Use `@Generable` with `respond(to:generating:)` instead of prompting for JSON and parsing manually. See `axiom-foundation-models` Scenario 2 for the complete migration pattern.

---

## Resources

**WWDC**: 286, 259, 301

**Skills**: axiom-foundation-models, axiom-foundation-models-diag

---

**Last Updated**: 2025-12-03
**Version**: 1.0.0
**Skill Type**: Reference
**Content**: All WWDC 2025 code examples included